class_docstr: A posterior based on GPyTorch's multi-variate Normal distributions.
docstr: The t-batch range.
  
  This is used in samplers to identify the t-batch component of the
  `base_sample_shape`. The base samples are expanded over the t-batches to
  provide consistency in the acquisition values, i.e., to ensure that a
  candidate produces same value regardless of its position on the t-batch.
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code: def batch_range(self) -> Tuple[int, int]:
      r"""The t-batch range.
  
      This is used in samplers to identify the t-batch component of the
      `base_sample_shape`. The base samples are expanded over the t-batches to
      provide consistency in the acquisition values, i.e., to ensure that a
      candidate produces same value regardless of its position on the t-batch.
      """
      if self._is_mt:
          return (0, -2)
      else:
          return (0, -1)
  
imports: from __future__ import annotations
  import warnings
  from contextlib import ExitStack
  from typing import Optional, Tuple, TYPE_CHECKING, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.posteriors.base_samples import _reshape_base_samples_non_interleaved
  from botorch.posteriors.torch import TorchPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator import settings
  from linear_operator.operators import BlockDiagLinearOperator, DenseLinearOperator, LinearOperator, SumLinearOperator
  from torch import Tensor
  from torch.distributions import Normal
label_desc: Use image node_id 4 for calling the GPyTorchPosterior obj's underlying member method code with example usage: obj.batch_range() and returns: int,  unknown, int,  unknown